goodness.metaMDS find goodness of fit measure for
points in nonmetric multidimensional scaling, and function
stressplot makes a Shepard diagram.## S3 method for class 'metaMDS':
goodness(object, dis, ...)
## S3 method for class 'default':
stressplot(object, dis, pch, p.col = "blue", l.col = "red",
lwd = 2, ...)monoMDS the default is
lwd = 1 if more than two lines are drawn, and lwd =
2 otherwise.goodness returns a vector of values. Function
stressplot returns invisibly an object with itmes for
original dissimilarities, ordination distances and fitted values.goodness.metaMDS finds a goodness of fit statistic
for observations (points). This is defined so that sum of squared
values is equal to squared stress. Large values indicate poor fit.
The absolute values of the goodness statistic depend on the
definition of the stress: isoMDS expresses
stress in percents, and therefore its goodness values are 100 times
higher than those of monoMDS which expresses the
stress as a proportion. Function stressplot draws a Shepard diagram which is a plot
of ordination distances and monotone or linear fit line against
original dissimilarities. In addition, it displays two
correlation-like statistics on the goodness of fit in the graph.
The nonmetric fit is based on stress $S$ and defined as $R^2
= 1-S^2$. The monoMDS, the
Both functions can be used with metaMDS,
monoMDS and isoMDS. The original
dissimilarities should not be given for monoMDS or
metaMDS results (the latter tries to reconstruct the
dissimilarities using metaMDSredist if
isoMDS was used as its engine). With
isoMDS the dissimilarities must be given. In
either case, the functions inspect that dissimilarities are
consistent with current ordination, and refuse to analyse
inconsistent dissimilarities. Function goodness.metaMDS is
generic in isoMDS which has no class.
metaMDS, monoMDS,
isoMDS, Shepard.data(varespec)
mod <- metaMDS(varespec)
stressplot(mod)
gof <- goodness(mod)
gof
plot(mod, display = "sites", type = "n")
points(mod, display = "sites", cex = 2*gof/mean(gof))Run the code above in your browser using DataLab